Numerous methods for recognizing patterns in data are known. See, e.g. Bishop, C. M. “Neural Networks for Pattern Recognition”, Oxford University Press, 1995, ISBN 0-19-853864-2; Michie, D., Spiegeihalter, D. J., Taylor, C. C. “Machine Learning, Neural and Statistical Classification” Ellis Horwood, 1994, ISBN 0-13-106360-X; Ripley, B. D. “Pattern Recognition and Neural Networks”, Cambridge University Press, 1996, ISBN 0-521-46086-7; and Webb, A. “Statistical Pattern Recognition” Arnold, 1999, ISBN 0-340-74164-3.
For the present purposes, these methods can be categorized into recognition methods for which (a) explicit rules are known and (b) rules are not known, but representative examples exist from which a mathematical relationship between causal input variables and some output variables can be derived. For the present purposes, a recognition algorithm of type (a) will be called rule-based and type (b) statistical.
The objective of a statistical recognition method is to produce a model, which can be applied to previously unseen inputs to classify the outcome correctly, without regard to compliance with explicit rules of any kind. In constructing such a model, there is an assumption that a meaningful (non-random) dependence exists between the input and output variables. There is often a further assumption that, for a given set of input variables, there may be more than one possible outcome. Such a model can still be useful if it correctly indicates a bias towards particular outcomes.
The output of these models is usually a set of numbers. These can be an attempt at calculating conditional probabilities (i.e. the probability of each outcome given the input variables) or simply an output-specific number, to be used in association with a threshold, above which the output is interpreted as being true. The choice of the threshold is usually dependent on the costs of misclassifications. Relaxing conditions needed to interpret results as conditional probabilities tends to produce better classification performance, meaning that the second type of model (unconstrained output-specific number) tends to classify better than the first.
An issue with statistical models is analogous to the interpretation of a sequence of noise-corrupted measurements into signal and noise components. If the model is to achieve its objective of correctly (or as near correctly as possible) classifying the outcomes of previously unseen data, it follows that unrepeatable fluctuations (noise), contained within the example set on which it is to be trained, must not be represented. In one way or another, this means that the complexity of a statistical model needs to be constrained so as to avoid modelling noise. Classically, this is achieved by dividing the data set into two parts, using one part to produce a number of models of systematically increasing complexity, examining errors associated with those models when applied to the unseen data set, and choosing the model complexity so as to minimise that error. There are a number of variations of this technique, which is known as cross-validation.
More recently, techniques have been developed (see, e.g. Bishop), which can reduce the output curvature of over-complicated models so as to maximize the inference (i.e. optimise the signal/noise decomposition) that can be drawn from a data set. The process of restricting output curvature is known as regularisation. These techniques have the advantage of producing models of optimal complexity for whatever data set is available, which makes them particularly useful when little data exists. In consequence, these methods are appropriate for recognition problems where either the recognition rules are not known, or for modelling a consensus view of experts who disagree about what the recognition rules should be.
The objective of rule-based recognition methods is to produce a model, which can be applied to previously unseen inputs to predict an outcome correctly, and comply with rules, which alone are sufficient for recognition.
Within computer science literature, these models are known as classical artificial intelligence. Typically, a knowledge engineer will elicit appropriate rules from an expert and program them. Such recognition methods are wholly deterministic and, unlike statistical recognition methods, do not encompass uncertainty. Neither do they encompass the idea of optimal inference from a noisy data set. They are appropriate therefore for situations that lack any differences of opinion and for which a clearly identifiable rule set is wholly adequate for recognition. There is a category of rule based problems that can be solved by rules e.g. whether or not well-defined criteria are satisfied so that a job application can move on to the next stage.
There is another category of problems that cannot easily be solved by rules and instead are addressed using statistical models. These problems include many medical problems, where the broad experience of a doctor has to be drawn on to reach a diagnosis. Often, it is the case that rules may very well be capable of being used with these problems but nobody knows what they are. The result is that a knowledge of the outcomes from similar instances to some present problem is used as a basis for a decision.
There may be an element of interpolation in this type of decision-making process, but essentially it draws on a base of known examples, believed to be relevant to a problem of interest. Given enough examples, multivariate statistical models can be built to replicate this type of decision-making process. These do not claim to know what the rules are, they simply claim to be a mathematical representation of the data (i.e. previous decisions and the factors they were based on), but are useful because they can encapsulate the experience that a professional, like a doctor, can build up over a working lifetime. They also have the additional advantage that they can process unlimited numbers of examples to encapsulate the experience of a whole generation of doctors if data is available.
Technical financial analysis (or just technical analysis), as opposed to fundamental analysis, uses the past price, volume activity, or other measures of a stock, or of a market as a whole, to predict the future direction of the stock or market. The results of technical analysis (sometimes also referred to as “charting”) are usually shown on charts or graphs that are studied by technicians to identify known trends and patterns in the data to forecast future performance.
A number of terms of art are used in the present specification. An inbound trend is a series of higher highs or lower lows that lead into a price pattern. An indicator is a calculation based on stock price and/or volume that produces a number in the same unit as price. An example of an indicator is the moving average of a stock price. An oscillator is a calculation based on stock price and/or volume that produces a number within a range. An example of an indicator is the moving average convergence/divergence (MACD). A price chart is a graph of a company's share price (Y-axis) plotted against units of time (X-axis).
The terms technical event, and fundamental event are coined terms to denote points such as the price crossing the moving average or the MACD crossing the zero-line. The technical event or fundamental event occurs at a specific point in time. The importance of most indicators and most oscillators can be represented as technical events. A technical event, as used herein, is the point in time where a stock price has interacted (e.g. crossed or bounced) with an indicator or a price pattern or an oscillator has crossed a threshold. There are other techniques that technical analysts use to interpret price history as well that can be represented as technical events. These, however, are more subjective and involve the subjective recognition of price formations or price patterns. Fundamental events are the point in time where a stock price has interacted (e.g. crossed or bounced) with a price value computed from company accounting and/or other economic data.
A price formation, price pattern or chart pattern is a pattern that indicates changes in the supply and demand for a stock cause prices to rise and fall. Over periods of time, these changes often cause visual patterns to appear in price charts. Predictable price movements often occur follow price patterns. A reversal pattern is a type of price pattern that is believed to indicated a change in the direction of a price trend. If prices are trending down then a reversal pattern will be bullish since its appearance is believed to indicate prices will move higher. Examples of bullish reversal patterns include double bottoms and head and shoulder bottoms. Similarly, if prices are trending up then a reversal pattern will be bearish. Examples of bearish reversal patterns include double tops and head and shoulder tops.
Traditionally, the approach to technical analysis is a manual one. One important aspect of technical analysis is pattern recognition in which price information for a period of time is graphed or plotted on a Cartesian coordinate system to facilitate visual recognition of established patterns. A manual approach to charting can be unreliable because it depends on human pattern recognition ability. It can be error prone due to guesswork, inaccurate heuristics or the absence of a systematic procedure for comparing the available data with all possible or likely formations. In addition, if the analyst has a predilection for certain formations, the results may be biased towards those formations and may not be as accurate as an unbiased approach. Finally, a manual approach, even with the aid of mechanical or computer assistance is inherently slow due to the human factor.
A recent innovation in technical analysis is the use of neural networks to recognize patterns in the financial data. However, training neural networks to recognize patterns, or formations, in financial results is cumbersome and highly dependent on the quality of data used to train the neural network.
One well-known technique in technical analysis is point and figure charting. In point and figure charting, the price of, for example, a stock is plotted as columns of rising Xs and falling Os to denote price movement greater than, or equal to, a threshold amount, denoted a box size. Unlike other charting methods, such as open, high, low, close (OHLC), bar or candlestick, where price action is plotted according to time, point and figure charting is more time independent and price, not time, dictates how point and figure charts take shape. For example, a series of volatile trading sessions over the course of a week could fill an entire page or screen in a point and figure chart, whereas a month of inactivity or static range trading might not be reflected on the chart, depending on the chosen box size. The box size determines how much background “noise” is removed from the price action, and, hence, the granularity of the resulting chart. The factors that typically influence the choice of box size include volatility and the time horizon being examined.
The technique of conventional point and figure charting is described in detail in Kaufman, P. J. “Trading Systems and Methods” ISBN 0-413-14879-2, John Wiley & Sons 1996. In summary, a box size, datum price and datum time, are chosen. If a new high exceeds the sum of the current datum plus a box size, a ‘X’ is written in a column and the datum price shifted to the datum plus box size. When the market reverses by more than some multiple of the box size, a column of Os is formed, and continues in a similar manner until the market reverses by more that the prescribed multiple of box sizes. The chart can be based on tick by tick results, or on the OHLC data. In conventional point and figure charting, the use of OHLC data can introduce ambiguity into the charting process, as a large price differentials between high and low in a single day can occur, potentially resulting in a reversal in both directions without it being clear whether the high or low occurred first.
One attractive feature of point and figure charting is the fact that conventionally accepted chart formations, such as double tops and triangles, can be clearly identified. Buy signals can be generated when prices surpass a previous bottom pivot point by one or more boxes, and the reverse for sell signals. This eliminates much of the subjectivity of other analysis techniques. However, point and figure charting is highly dependent on the box size chosen, and relevant formations can be missed if the box size is not appropriate. Some points to note are: (1) point and figure charting conventionally works forwards from a datum rather than backwards from the end of the series. This means that the sequence of X's and O's required to generate a trading pattern depends on the date and price used to start the sequence—which usually results in delayed pattern completion dates, depending on how fortunate the choice of origin was (2) the intention is to produce a chart using a fixed box size, from which a formation will hopefully be recognised visually; (3) the box size acts as a filter, in that small fluctuations in value do not trigger the creation of either a new ‘X’ or ‘O’, but large fluctuations do; and (4) point and figure charts are independent of time, but to create a zig-zag line, time is required. Products available for automating point and figure charting suffer similar disadvantages.
An alternative method is the use of pivot points in the technical analysis of a time series. The time series can include time series of financial data, such as stock prices, medical data, electrocardiogram results, or any other data that can be presented as a time series, and in which it is desirable to identify turning points, trends, formations or other information. The method of pivot points uses a modified point and figure technique to determine the pivot, or turning points, and categorizes them according to the box size at which they appear, while associating time, or lag, information with each identified point. A method of pivot point characterization in technical analysis, and a method for using the characterized pivot points for price formation recognition, are described in U.S. patent application Ser. No. 10/245,240 and U.S. patent application Ser. No. 10/245,263, both filed Sep. 17, 2002, the contents of which are incorporated herein by reference.
Price formations used by traders can, in part be defined by specifications. Using specifications alone, it is difficult, if not impossible, to achieve consistent recognition of formations to satisfy a consensus of traders. There is a category of problems for which experience offers better solutions than rule-driven specifications. To try to devise perfect rules to recognize trading formations may be successful for a few carefully chosen examples, but the resulting recognition method would be likely to perform poorly on a random selection of previously unseen formations—the reason being that a generic recognition rule set cannot easily be identified and may not even exist for this type of problem. It is, therefore, desirable to provide a recognition model that has adequate complexity to recognize formations in general.